Staff Data Engineer
Role Overview
The Staff Data Engineer will develop Tempcover’s cloud data platform in partnership with the Data Engineering team.
The team is building a new platform on Google BigQuery, Dagster+, and dbt, while supporting and gradually decommissioning the existing Azure platform over 12 months.
The redesign aims to create an AI-first data platform for analysts and stakeholders and support marketing, pricing, product, and commercial decisions.
Key Skills for This Role
Full Job Posting
Work arrangement
- Hybrid: two days per week in the Tower Bridge office. The role can also be based in the Fleet office.
About Tempcover
Tempcover operates in short-term insurance and aims to make car insurance flexible, quick, and easy for drivers.
The Data and Analytics team provides data, management information, and insight for internal and external stakeholders and partners.
Role overview
The Staff Data Engineer will develop Tempcover’s cloud data platform in partnership with the Data Engineering team.
The team is building a new platform on Google BigQuery, Dagster+, and dbt, while supporting and gradually decommissioning the existing Azure platform over 12 months.
The redesign aims to create an AI-first data platform for analysts and stakeholders and support marketing, pricing, product, and commercial decisions.
What you’ll do
- Help define platform architecture and engineering standards, and influence technical direction.
- Deliver high-value BigQuery capabilities incrementally to demonstrate value and support future AI and advanced analytics.
- Build reliable, tested, idempotent ELT pipelines using BigQuery, dbt, and Dagster+.
- Create performant data models and improve platform quality, performance, observability, and lineage.
- Support the existing Azure platform while the organisation transitions away from it.
- Collaborate with analysts and business stakeholders to deliver data solutions and ensure trusted, available data.
- Mentor and guide junior data engineers.
What they’re looking for
- Advanced SQL, including complex queries, partitioning, clustering, and cost-aware optimisation.
- Experience with modern orchestration and ELT tooling; Dagster and dbt are preferred.
- Strong Python for data engineering and solid knowledge of warehousing, ETL/ELT, and dimensional modelling.
- Experience designing data models and documentation for AI/LLM analytics, including semantic layers, metadata, and access controls.
- In-depth cloud data infrastructure knowledge; GCP is preferred, with Azure or AWS also considered.
- Strong problem-solving and ability to work independently and collaboratively.
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